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08/10/2023
Unifying Distributionally Robust Optimization via Optimal Transport Theory
In the past few years, there has been considerable interest in two promi...
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06/07/2023
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
We develop a principled approach to end-to-end learning in stochastic op...
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05/30/2023
Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets
We propose a policy gradient algorithm for robust infinite-horizon Marko...
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03/07/2023
New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
We study optimal transport-based distributionally robust optimization pr...
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06/27/2022
Stability Verification of Neural Network Controllers using Mixed-Integer Programming
We propose a framework for the stability verification of Mixed-Integer L...
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05/30/2022
Metrizing Fairness
We study supervised learning problems for predicting properties of indiv...
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03/02/2022
Discrete Optimal Transport with Independent Marginals is #P-Hard
We study the computational complexity of the optimal transport problem t...
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06/12/2021
Distributionally Robust Optimization with Markovian Data
We study a stochastic program where the probability distribution of the ...
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06/08/2021
Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Training models that perform well under distribution shifts is a central...
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06/01/2021
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Least squares estimators, when trained on a few target domain samples, m...
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03/10/2021
Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution
Semi-discrete optimal transport problems, which evaluate the Wasserstein...
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02/06/2021
Efficient Learning of a Linear Dynamical System with Stability Guarantees
We propose a principled method for projecting an arbitrary square matrix...
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12/09/2020
A Statistical Test for Probabilistic Fairness
Algorithms are now routinely used to make consequential decisions that a...
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07/18/2020
A Distributionally Robust Approach to Fair Classification
We propose a distributionally robust logistic regression model with an u...
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04/15/2020
On Linear Optimization over Wasserstein Balls
Wasserstein balls, which contain all probability measures within a pre-s...
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11/08/2019
Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization
We introduce a distributionally robust minimium mean square error estima...
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10/23/2019
Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation
The likelihood function is a fundamental component in Bayesian statistic...
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10/17/2019
Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
A fundamental problem arising in many areas of machine learning is the e...
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08/23/2019
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Many decision problems in science, engineering and economics are affecte...
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03/07/2019
RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems
Nonlinear optimal control problems are often solved with numerical metho...
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09/24/2018
Wasserstein Distributionally Robust Kalman Filtering
We study a distributionally robust mean square error estimation problem ...
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06/28/2018
Automatic Exploration of Machine Learning Experiments on OpenML
Understanding the influence of hyperparameters on the performance of a m...
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05/18/2018
Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
We introduce a distributionally robust maximum likelihood estimation mod...
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10/27/2017
Regularization via Mass Transportation
The goal of regression and classification methods in supervised learning...
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05/22/2017
Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization
Plain vanilla K-means clustering is prone to produce unbalanced clusters...
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09/30/2015